Explore a Python framework for cryptocurrency algorithmic trading that uses composable boolean functions for strategy design, supporting live trading, simulation, and backtesting.
This repo teaches algorithmic trading with a disciplined Research-Backtest-Implement method using Python and AI tools. It stresses avoiding emotional mistakes with systematic workflows.
QSTrader offers a modular Python backtesting framework for long-short equity strategies using daily OHLC data and calendar-driven rebalancing. Its clean separation of signal, portfolio, and execution components stands out.
A curated GitHub repo consolidates 200+ quality resources for quantitative and ML-driven algorithmic trading, bridging academic research and practical strategies.
ai-trader adds natural language AI interaction to algorithmic trading backtesting via an MCP server and YAML configs. Supports US/TW stocks, crypto, forex with caching.
This curated repo maps the shift in systematic trading from event-driven backtesters to AI-powered strategy discovery, covering multi-asset tools, high-frequency backtesting, and AI agents.
FinRL-Trading offers a modular Python framework for quantitative trading focused on a weight-centric architecture unifying backtesting and live execution, with classical and DRL portfolio methods.
QuantDinger unifies AI-assisted research, Python strategy development, backtesting, and live trading in a self-hosted platform with scoped AI agent tokens and strict safety defaults.
Lumibot unifies backtesting and live trading for stocks, options, crypto, and forex with AI agent runtime using DuckDB, supporting multiple brokers and data sources.
QuantaAlpha uses large language models with evolutionary strategies to automate quantitative alpha factor discovery, achieving strong backtest metrics on major indices.
